The Fallout of Catastrophic Technogenic Emissions of Toxic Gases Can Negatively Affect Covid-19 Clinical Course

The coronavirus D-19 (Covid-19) pandemic has shaken almost every country in the world: as we stand, 6,3 million deaths from the infection have already been recorded, 167,000 and 380,000 of which are in Italy and the Russian Federation, respectively. In the first wave of the pandemic, Italy suffered an abnormally high death toll. A detailed analysis of available epidemiological data suggests that that rate was shockingly high in the Northern regions and in Lombardy, in particular, whilst in the southern region the situation was less dire. This inexplicably high mortality rate in conditions of a very well-developed health care system such as the one in Lombardy – recognized as one of the best in Italy – certainly cries for a convincing explanation. In 1976, the small city of Seveso, Lombardy, experienced a release of dioxin into the atmosphere after a massive technogenic accident. The immediate effects of the industrial disaster did not become apparent until a surge in the number of tumors in the affected population in the subsequent years. In this paper, we endeavor to prove our hypothesis that the release of dioxin was a negative cofactor that contributed to a worsening of the clinical course of COVID-19 in Lombardy.


INTRODUCTION
The coronavirus D-  pandemic has hit almost every country in the world as it has spread west from China to Europe, the U.S., and later South America, Africa, and the Russian Federation. At this juncture, 6.3 million COVID-19 deaths have been reported worldwide, with 380,000 of those in the Russian Federation alone. The impact of the pandemic has been particularly severe in several European countries, such as Spain, France, Belgium, the UK, and Italy, whilst other countries such as Portugal, Germany, the Scandinavian states, and Eastern Europe, in general, have had it relatively easy. Italy, in particular, has had a shockingly high mortality rate, one that significantly exceeded the death rate observed in the rest of the world.
But a closer look at the epidemiological data would suggest that this high rate was mainly concentrated just in the Northern regions, in Lombardy in particular, whilst in the southern region the clinical course of most patients was more favorable, as our group had predicted well in advance [1]. This situation is particularly troubling if the general mortality rate is compared with the one that prevailed in the previous five years. This inexplicably high mortality rate in the context of a very well-developed health care system such as the one in Lombardy -largely recognized as one of the best, if not the best, in the coun-try [2] -certainly calls for a satisfactory explanation. Some experts have directed their attention at the potential negative role of PM-10, which are overrepresented in Lombardy [3] and some neighboring regions also significantly affected by the Covid-19 pandemic. However, if we assume that this hypothesis is sound, it becomes hard to explain why California, which is highly polluted and seriously affected by PM-10, appeared definitely less affected than other states, with New York first in mind, where the air concentration of PM-10 is lower.
In 1976 the small city of Seveso, which is relatively close to Brescia, Bergamo and Milan, became sadly known in the world for an accidental escape of dioxin. The immediate effects were mild, but that was before an increased number of tumors began to appear in the affected population in subsequent years [4]. In our work, we hypothesize that the gas escape had a negative cofactor role in the worse clinical course of the Covid-19 pandemic for patients in Lombardy.

EXPERIMENTAL
We conducted a study correlating the distance from the epicenter of the escape of dioxin, Seveso, to the rate of mortality of the potentially affected provinces. We studied the local mortality rate from the Covid-19 infection as a percentage of the dead vs. infected patients and compared data for Lombardy with those for other world regions where a dramatic leak of toxic gases had occurred: the city of Bhopal (India), where a significant accidental toxic gas release occurred in 1980 from a local Union Carbide factory, something that was considered at the time as the worst industrial disaster in history [5,6].
We retrieved data about the local weather conditions and winds directions at the time of the accidents and also calculated the distance between the sites of the escapes and those most clinically affected. We also analyzed the air pollution of the three sites as measured by the PM-10 concentration. The gases were in fact different: 2,3,7,8-tetrachlorodibenzodioxin in the case of Seveso [7] and methyl isocyanate in the case of Bhopal [8][9][10]. Nevertheless, both gases are known to be mutagenic and cancerogenic [7,[11][12][13][14][15].
In both scenarios the analysis of local weather conditions allowed us to somewhat reconstruct the possible spread of the escaped gases on account of the effects of the winds. In the Seveso case, nice weather conditions in Lombardy, together with high pressure in the Alps (Fig. 1A), favored the Mistral pushing the gases south east; i.e., in the direction of Bergamo, Brescia and further south up to the western provinces of Veneto and Emilia Romagna. Some other components of the Mistral could also have pushed the gas towards eastern Piemonte and the northern part of Liguria (Fig. 1B). In the Bhopal region, where the ac- Gregale cident occurred in December, the Monsoons typically flow from north to the southwest ( Fig. 2A): so, the gases escape would have spread from the Bhopal region of Madhya Pradesh to the neighboring state of Maharashtra (Fig. 2B). As far as Italy was concerned, we also considered the possible impact of Chinese immigrants, as well as the course of the infection vs. the density of the local population.

DATA SOURCES
We used data available in several public databases. Table 1 shows the distribution of the 2020 death rate compared with previous years as reported by ISTAT, the Italian Institute of statistics. The increase is particularly notable in the north, specifically in Lombardy [1]. Graphic is reported in Fig. 3. Data from Chinese immigration in Italy are also from ISTAT, which also provided data on the local population. Data on the infection incidence (https://github.com/pcmdpc/COVID-19) come from the official GitHub repository of the Italian government [16] and is represent-ed in Fig. 4. The data on infections and the death rate in India come from publicly available sources and reports of the death rate in the potentially affected regions of India as compared to the rest of the country ( Table 2). Data regarding the PM-10 concentration in Italy were retrieved from the repository of ISPRA (Istituto Superiore per la Protezione e la Ricerca Ambientale/Italian Superior Institute for Protection and Environmental Research) [17]. Figure 5 shows the level of PM-10 concentration in Europe. It shows how its concentration was increased over the Padana landscape [18].

STATISTICAL ANALYSIS
To analyze the data, we used the non-parametric Spearman's Rank correlation coefficient [19]. Such a method includes no assumption on the underlying data, apart from being at least on an ordinal scale, which is always the case in our analyses. As the threshold of significance, we considered an α-level of 0.05, as customary. In the case where multiple    hypotheses were being considered, we applied the Bonferroni correction [20]. In the case of an analysis of multiple factors, we used ANOVA [21]: again, considering the α-level mentioned above.

Chinese Immigration
The presence of immigrants from China in Italy (https://www.tuttitalia.it/statistiche/cittadini-stranieri/repubblica-popolare-cinese/) is not a factor in the spread of the virus [22].

Population Density
Social proximity does not appear to affect the contagion and the death rate in Italy. We found no significant nonparametric correlation between the density of the population and the increase in mortality with respect to the last five years average (the p values are 0.083 and 0.071 respectively, indeed not significant), or between density and infection spread (0.17; again, absolutely not significant).

Influence of the PM-10 level
The PM-10 appears to have an effect considering the number of days above the threshold in Italy and, in particular, in Lombardy. There is a correlation of 0.40 with the number of deaths in 2019 (p < 10 -4 ) and a correlation of 0.38 with the 5-year average of number of deaths (p < 10 -3 ). There is also a correlation of 0.41 with the percentage of infected people (p < 10 -4 ). However, if we consider together the effects of the distance from Seveso and the presence of PM-10 in a ranked ANOVA, we observe that the distance from Seveso retains its significance (t = -15.57, p < 10 -8 ), while the presence of PM-10 does not.

Distance from Seveso and Bhopal
The distance from Seveso appears to be a determining factor (Fig. 3). In terms of increase in deaths with respect to 2019 we found a very strong correlation: -0.82 (p < 10 -24 ), whilst with respect to the average of the last five years it was -0.83, (p < 10 -25 ). In terms of the percentage of infected population, the correlation is even higher, at -0.88 (p < 10 -32 ). In summary, the closer to Seveso the analyzed sites were, the higher the rate of infected population and Covid-19-related deaths were. In India, the correlation be- tween distances from Bhopal is significant on both the reported percentage of deaths due to coronavirus (-0.52, p < 0.01) and of infected people (-0.36, p < 0.05) ( Table 3).

DISCUSSION
The possibility of a toxic gas escape that occurred 40 years ago playing a role in the increased incidence of complicated clinical courses in the recent Covid-19 infection is an intriguing, albeit difficult to demonstrate, hypothesis. As a result of both accidents, two different toxic gases were released, but both gases were characterized by high carcinogenicity [7, 11-15, 23, 24]. An increased mortality rate from COVID-19 was observed in all regions potentially exposed to the gases spread by the winds prevailing at the time of the accident. An increased mortality rate due to the Covid-19 infection was witnessed in all the regions potentially touched by the gas leaks. This is also intimated in the observation of the possible effects of the winds active in those particular times of the year. This death rate increase was particularly striking in Lombardy, a fact that continues to require a plausible explanation. The particularly high virulence of the virus that affected the North of Italy was claimed as a possible reason for the high death toll [1]. Even if we assume that the better clinical course observed in the southern Italian regions was the result of heeding the lessons learned when the disease coursed through the northern parts of the country, the mortality rate difference remains hard to explain.
The possible detrimental effects of the PM-10 pollution has been invoked as a negative factor that has aided a more aggressive clinical course of the epidemic due to its chronic irritative impact on the respiratory system [25]. However, as we noted above, this hypothesis is somewhat contradicted by the observation that the impact of the epidemic in California has been definitely milder than it has been in New York, although the air concentration of PM-10 is much higher in California [26]. So, the detrimental effect of PM-10 pollution cannot be the sole reason for what was observed in Lombardy.
Other claims refer to the presence of immigrants from China. The available data from ISTAT show that on January 1, 2019, the number of Chinese present in Milan was higher than that in Rome. However, the provinces with the highest increase in deaths had lower numbers of Chinese immigrants. There are also claims that social proximity increases the contagion rate and, consequently, the death rate. However, we found that density did not push the mortality rate upward as relates to 2019 and to the last five years. Also, if we consider the number of infections, in this case there is also no significant correlation.
We hypothesize that the fallout of the Seveso accident -perhaps in addition to the detrimental effects of air pollution -would have acted synergically in Lombardy to make the clinical course of the coronaviral infection there particularly aggressive. It may have acted not only by predisposing residents, as a consequence of air pollution's effect on the respiratory system of Lombard patients, to viral attacks, in particular to a significantly more aggressive course of the autoimmune reaction towards the alveoli the virus induces, but also through some gene-modifying mechanism that had taken place during the preceding 45 years and acted somehow by reinforcing the aforementioned autoimmune process. The other case, Bhopal, India, experienced an increased mortality rate as compared to the rest of the country. However, this difference, albeit significant, was not as striking as the one observed in Lombardy. We would venture that, in the region of Bhopal, the air concentration of PM-10 is not as significant as it is in the Padana landscape, which is a well-known site of significantly polluted air.
To support our claims, we used the robust Spearman's Rank correlation coefficient. We considered first the relationship between the unequivocal number of variation of deaths in relationship to the previous years. The resulting value of the correlation of the distance from Seveso and the increase in deaths with respect to 2019 is impressive (-0.82, p < 10 -24 ), and it is even more impressive with respect to the average for the last five years (-0.83, p < 10 -25 ). We have also considered the relationship between distance to Seveso and the percentage of the infected population, and in this case the correlation is even higher -0.88 (p < 10 -32 ).
For conclusiveness, we have also considered the claimed effect of PM-10, particularly by calculating the number of days above the safe threshold. We have noticed that, indeed, there is an impact, by far below the one related to the distance from Seveso (0.40, р < 10 -4 ). The average number of deaths over 5 years (0.38, p < 10 -3 ) and the percentage of those infected with COVID-19 (0.41, p < 10 -4 ) also correlated with elevated levels of PM-10. We have built a ranked ANOVA to attempt to determine the joint contribution of the number of days of PM-10 above the threshold and the distance from Seveso. In performing such an analysis, we arrived at the conclusion that the distance from Seveso remains highly statistically significant, while the number of days of PM-10 above the threshold completely loses such significance.
To bolster our hypothesis, we turned our attention to the case of India, where we directly tested the presence of a correlation between the distance from Bhopal and the reported rate of infected and dead people from Coronavirus. The historical data on the total number of deaths and on the presence of PM-10 was not available to us. So, we had to rely only on the public data specific to the disease in 2020. In this case, we also found a statistically significant Spearman's rank correlation between the distance from Bhopal and the percentage of infected people (-0.36, p < 0.05), as well as that of dead people (-0.52, p < 0.01).

CONCLUSIONS
Our hypothesis, obviously, requires confirmation, perhaps through a study comparing certain genom-